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Academic Journal of Agricultural Sciences, 2022, 3(3); doi: 10.38007/AJAS.2022.030305.

Corn Growth Model Based on Yield Statistical Model

Author(s)

Adityan Kumare

Corresponding Author:
Adityan Kumare
Affiliation(s)

Griffith University, Australia

Abstract

Corn is one of the main agricultural crops in China, but the agricultural technology conversion rate of corn cultivation is not high. The growth cycle of corn is longer and the process is more complicated. In addition to being affected by internal physiological mechanisms, the growth of maize has a great influence on its growth environment, especially the light environment. During the growth process, the interaction between corn and light environment is constantly underway. Corn planting time is also an important factor affecting corn yield. The difference in sowing date directly affects the growth and development stages of corn. The purpose of this paper is to study the growth pattern of corn based on a statistical model of yield. In terms of methods, it is proposed to use the inverse ray tracing algorithm to calculate the light energy reflected and absorbed by the corn, and establish a model to analyze the sensitivity of the area, mainly in terms of leaf area index, temperature, and moisture to analyze corn yield. Establish a corn growth model, and construct it from four aspects: yield, photosynthesis, temperature, and moisture. Finally, comprehensive supplements were made, and planting conditions continued to be optimized at planting density. In terms of experiments, the meteorological data and soil parameters of the plantation site were investigated. Finally, the experiment was divided into four groups, one was for home planting; the other was for planting at different intervals, the other was normal; the three were for drip irrigation, and the other was normal Four groups were planted using the improved strategies proposed in this paper. It is concluded that under drip irrigation conditions, the grain filling rate can be significantly increased, which will lead to an increase in 100-grain weight during maturity. With the increase of the population density, the competition among individuals within the group for light, temperature, water, and fertilizer is intensified. For better individual development, the individual plant height and ear height have been continuously increased in order to obtain more light energy resources. This is the instinctive response of plants to avoid each other. It was concluded that the planting density of corn reached its maximum under the condition of 80,000 plants / ha, and when the density increased further, the yield began to decrease. The improved planting model in this paper is superior to the other three planting models, followed by planting at different densities, followed by drip irrigation and normal planting.

Keywords

Corn Planting, Corn Growth Model, Model Analysis, Yield Statistics Model

Cite This Paper

Adityan Kumare. Corn Growth Model Based on Yield Statistical Model. Academic Journal of Agricultural Sciences (2022), Vol. 3, Issue 3: 54-68. https://doi.org/10.38007/AJAS.2022.030305.

References

[1] Bingbing Xia, Huiyan Jiang, Ziran Wang.(2017). “Research on Building High-Speed Statistical Shape Model Using Liver Cell Images”, Journal of Medical Imaging & Health Informatics, 7(3),pp.561-567.https://doi.org/10.1166/jmihi.2017.2053

[2] F. Fan, X. Xing, W. Jiang.(2019). “Optimization of Pyrolysis Carbonization Conditions Based on Energy Yield Analysis for Corn Stover Pellets”, Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 40(1),pp.172-178.

[3] Cécile Hardouin, Noel Cressie.(2018). “Two-scale Spatial Models for Binary Data”, Statistical Methods & Applications, 27(2),pp.1-24.https://doi.org/10.1007/s10260-017-0391-1

[4] Y Sánchez-Roque, Y D C Pérez-Luna, E Pérez-Luna.(2017). “Evaluation of Different Agroindustrial Waste on the Effect of Different Carcass Characteristics and Physiological and Biochemical Parameters in Broilers Chicken”, Veterinary World, 10(4),pp.368.https://doi.org/10.14202/vetworld.2017.368-374

[5] Yi Zhang, Yanxia Zhao, Chunyi Wang.(2017). “Using Statistical Model to Simulate the Impact of Climate Change on Maize Yield with Climate and Crop Uncertainties”, Theoretical & Applied Climatology, 130(3-4),pp.1065-1071.https://doi.org/10.1007/s00704-016-1935-2

[6] V. G. Astafurov, A. V. Skorokhodov.(2018). “Statistical Model of Physical Parameters of Clouds Based on MODIS Thematic Data”, Izvestiya Atmospheric and Oceanic Physics,54(9),pp.1202-1213.https://doi.org/10.1134/S0001433818090049

[7] Mei Hong, Xi Chen, Ren Zhang.(2017). “Forecasting Experiments of a Dynamical-Statistical Model of the Sea Surface Temperature Anomaly Field Based on the Improved Self-Memorization Principle”, Ocean Science,14(2),pp.1-64.https://doi.org/10.5194/os-14-301-2018

[8] Y.-S. Liu, X.-M. Yang, G.-J. Li.(2017). “Investigation on Statistical Model and Analysis Method for Spatial Directional Distribution of Vessel Radiated Noise Based on Optimization Regression Theory”, Journal of Ship Mechanics, 21(11),pp.1431-1439.

[9] Silvia Caldararu, Drew W. Purves, Matthew J. Smith.(2017). “The Impacts of Data Constraints on the Predictive Performance of a General Process-Based Crop Model (PeakN-crop v1.0)”, Geoscientific Model Development, 10(4),pp.1679-1701.https://doi.org/10.5194/gmd-10-1679-2017

[10] Leng J , Zang J , Yan X , et al.(2017). “Turbulence Model Evaluation in Circular Pipe of Supercritical Water Based on Statistical Method”, Nuclear Power Engineering, 38(4),pp.11-15.

[11] Jun L , Wei G , Xudong W , et al.(2017). “Study on High Accuracy Measurement Model of Pressure Sensor Based on Ridge Regression”,Chinese Journal of Sensors & Actuators, 30(3),pp.391-396.

[12] X. Xu, R. Li, X. Wu.(2018). “Model and Calculation of Carbon Footprint for Station of Cold Chain Based on Time Dimension of Product Life Cycle”, Computer Integrated Manufacturing Systems,  24(2),pp.533-538.